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tickernelz

FastApply MCP Server

by tickernelz
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.1.1

  • Disambiguation5/5

    The two tools have clearly distinct purposes: call_tool handles tool execution, while list_tools provides metadata about available tools. There is no overlap or ambiguity between these functions, making it easy for an agent to select the correct tool.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (call_tool and list_tools) with the same snake_case convention. This predictability aids in understanding and usage without any deviations.

    Tool Count2/5

    With only 2 tools, this server feels thin and under-scoped for most practical applications. While it covers basic MCP functionality, it lacks domain-specific operations that would justify a dedicated server, making the count too low for effective agent use.

    Completeness1/5

    The tool surface is severely incomplete for any meaningful domain. It only provides meta-operations (calling and listing tools) without any actual functionality for a specific purpose, leaving obvious gaps that will cause agent failures in real-world tasks.

  • Average 2.8/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description carries full burden for behavioral disclosure. 'Handle tool calls' reveals almost nothing about the tool's behavior - it doesn't indicate whether this executes tools, validates calls, routes requests, or performs other operations. It provides no information about permissions, side effects, error handling, or response characteristics.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is maximally concise at just three words. There's no wasted language or unnecessary elaboration. While this conciseness comes at the expense of clarity, the description itself is efficiently structured with zero redundancy.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given that this appears to be a core tool invocation mechanism with 2 required parameters, nested objects in the schema, and an output schema, the description is completely inadequate. 'Handle tool calls' doesn't explain what the tool does, how to use it, what it returns, or how it relates to the sibling 'list_tools' tool. The presence of an output schema helps, but the description provides insufficient context for effective use.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, so the description must compensate for the completely undocumented parameters. The description provides no information about what 'name' and 'arguments' represent, their expected formats, or how they should be used. With 2 required parameters that have no schema documentation, this represents a significant gap.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose2/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Handle tool calls' is tautological - it essentially restates the tool name 'call_tool' without adding meaningful specificity. It doesn't clarify what 'handle' means in this context (invoke? execute? manage?) or what types of tools are being called. While it distinguishes from 'list_tools' by implying action rather than listing, the purpose remains vague.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No guidance is provided about when to use this tool versus alternatives. The description doesn't indicate whether this is for invoking specific tools, executing tool workflows, or managing tool calls. With a sibling tool 'list_tools' available, there's no indication of the relationship between listing tools and calling them.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries full burden but only states what the tool does without behavioral details. It doesn't disclose if it's read-only, has rate limits, authentication needs, or what metadata format is returned, which is insufficient for a tool with zero annotation coverage.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, clear sentence with no wasted words, effectively front-loading the core functionality. It's appropriately sized for a simple tool with no parameters.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool has 0 parameters, 100% schema coverage, and an output schema exists, the description is minimally adequate. However, it lacks behavioral context and usage guidelines, which are gaps despite the structured data covering parameters and outputs.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add param info, but that's acceptable here, warranting a baseline score above 3 due to the lack of parameters.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the verb ('Return') and resource ('metadata for available tools'), making the purpose unambiguous. However, it doesn't differentiate from its sibling 'call_tool', which appears to be an execution tool versus this metadata retrieval tool, missing explicit sibling distinction.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No guidance is provided on when to use this tool versus alternatives. The description lacks context about its role relative to 'call_tool' or any prerequisites, leaving usage unclear beyond the basic purpose.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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